Perspective data analysis and management

ABSTRACT

A system and computer implemented method for managing perspective data is disclosed. The method may include collecting a first lot of perspective data for an item. The method may include introducing a variant feature to the item to constitute a modified item. The method may include collecting a second lot of perspective data for the modified item. The method may also include evaluating the first and second lots of perspective data to ascertain a sentiment fluctuation based on information relevant to the variant feature.

BACKGROUND

The present disclosure relates to computer systems, and morespecifically, managing perspective data.

The amount of data available on the Internet and other communicationnetworks is growing rapidly. Perspective data, such as reviews,editorials, commentaries, social media and the like are examples ofcontent available to users. As the amount of available informationincreases, the need for managing perspective data may also increase.

SUMMARY

Aspects of the present disclosure, in certain embodiments, are directedtoward a system and method for managing perspective data. In certainembodiments, the method may include collecting a first lot ofperspective data for an item. In certain embodiments, the method mayinclude introducing a variant feature to the item to constitute amodified item. In certain embodiments, the method may include collectinga second lot of perspective data for the modified item. In certainembodiments, the method may include evaluating the first and second lotsof perspective data to ascertain a sentiment fluctuation based oninformation relevant to the variant feature.

The above summary is not intended to describe each illustratedembodiment or every implementation of the present disclosure.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The drawings included in the present application are incorporated into,and form part of, the specification. They illustrate embodiments of thepresent disclosure and, along with the description, serve to explain theprinciples of the disclosure. The drawings are only illustrative ofcertain embodiments and do not limit the disclosure.

FIG. 1 is a flowchart illustrating a method for managing a set ofperspective data, according to embodiments;

FIG. 2 is an illustration of an example implementation of a method formanaging perspective data, according to embodiments;

FIG. 3 is a diagram illustrating an example system architecture formanaging perspective data, according to embodiments;

FIG. 4 is a flowchart illustrating a method for managing perspectivedata, according to embodiments; and

FIG. 5 depicts a high-level block diagram of a computer system forimplementing various embodiments of the present disclosure, according toembodiments.

While the invention is amenable to various modifications and alternativeforms, specifics thereof have been shown by way of example in thedrawings and will be described in detail. It should be understood,however, that the intention is not to limit the invention to theparticular embodiments described. On the contrary, the intention is tocover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the invention.

DETAILED DESCRIPTION

Aspects of the present disclosure relate to various embodiments of asystem and method for managing perspective data. The perspective datamay, for example, include a first set of reviews. More particularaspects relate to analyzing semantic and syntactic content of a firstset of reviews using a natural language processing technique. The methodmay include identifying a variant feature of an item having a first setof reviews. Based on the variant feature, the method may includegrouping the first set of reviews into a first group and a second group.The method may include determining a first set of relevancy scores forthe first group and a second set of relevancy scores for the secondgroup. The method may also include establishing, using at least one ofthe first and second relevancy scores, a second set of reviewsconfigured to be a subset of the first set of reviews.

In recent years, the increased availability and access to large amountsof content via the Internet, social media, and other networks haveresulted in an increase in the need for organizing and evaluating thatcontent. Reviews are one form in which opinions, commentaries, andperspectives regarding the quality or merit of an item can be expressed.Aspects of the present disclosure relate to the recognition that overtime, updates, revisions, and other sorts of changes to items may renderparticular reviews less relevant to certain audiences of readers. Forexample, an item such as a product may receive a mid-cycle design changeto a component that may impact the quality and reviews regarding theitem. Similarly, a change in hotel or restaurant service may influencethe subsequent reviews and opinions written by patrons. Accordingly,aspects of the present disclosure are directed toward identifying thesevariations (e.g., variant features) of an item, and using them to manageand organize the reviews for the item. These item variations (e.g.,variant features) may take one of a number of forms depending on thenature of the item, including supplier changes, component changes,management/staff changes, renovations, menu changes, and the like.Aspects of the present disclosure may be associated with benefitsincluding review organization, content relevance, time saving, andefficiency.

Aspects of the present disclosure relate to various embodiments of asystem and method for managing perspective data. In certain embodiments,the perspective data may include a first set of reviews. More particularaspects relate to analyzing semantic and syntactic content of the firstset of reviews using a natural language processing technique. The methodand system may work on a number of devices and operating systems.Aspects of the present disclosure, in certain embodiments, includeidentifying a variant feature of an item having a first set of reviews.The variant feature may be configured to change with respect to apredetermined fixed criterion. In certain embodiments, identifying thevariant feature of an item having a first set of reviews may includeparsing the first set of reviews. The first set of reviews may be parsedby a natural language processing technique configured to analyzesemantic and syntactic content. In response to parsing the first set ofreviews, the method may also include determining a set of sharedcharacteristics of the set of reviews. The method may also includeselecting a first shared characteristic of the first set of reviews asthe variant feature. In certain embodiments, selecting the first sharedcharacteristic as the variant feature may include determining, based onsemantic information analyzed by the natural language processingtechnique, that the first shared characteristic has a pertinence valuegreater than a pertinence threshold.

Aspects of the present disclosure include grouping, based on the variantfeature, the first set of reviews into a first group and a second group.In certain embodiments, grouping the reviews may include sorting, intothe first group, reviews of the first set of reviews that are coupledwith a first variable element of the variable feature. The firstvariable element of the variable feature may correspond to the fixedcriterion. In certain embodiments, grouping the reviews may includesorting, into the second group, reviews of the first set of reviews thatare coupled with a second variable element of the variable feature. Thesecond variable element of the variable feature may correspond to thefixed criterion.

Aspects of the present disclosure, in certain embodiments, are directedtoward determining a first set of relevancy scores for the first groupand a second set of relevancy scores for the second group. In certainembodiments, determining the relevancy scores may include parsing thefirst group using a natural language processing technique configured toanalyze semantic and syntactic content. The method may also includecalculating, based on syntactic content, semantic content, and metadatafor the first group, the first set of relevancy scores. The method mayalso include assigning the first set of relevancy scores to the firstgroup. In certain embodiments, calculating the first set of relevancescores may include weighting the first set of relevance scores based ona triggering event linked to the variant feature.

Aspects of the present disclosure, in certain embodiments, are directedtoward establishing, using at least one of the first and secondrelevancy scores, a second set of reviews configured to be a subset ofthe first set of reviews. In certain embodiments, establishing thesecond set of reviews may include determining that the first set ofrelevancy scores of the first group is greater than a first relevancythreshold, and that the second set of relevancy scores of the secondgroup is lower than the first relevancy threshold. In response todetermining that the second set of relevancy scores of the second groupis lower than the first relevancy threshold, the method may includefiltering the first set of reviews to remove the second group. Furtheraspects of the present disclosure are directed toward generating achronological representation for the first set of reviews in relation tothe variant feature, the first variable element, and the second variableelement.

Turning now to the figures, FIG. 1 is a flowchart illustrating a method100 for analyzing and managing a set of item reviews, consistent withembodiments of the present disclosure. Aspects of FIG. 1 are directedtoward establishing a second set of reviews using relevancy scores and avariant feature of a first set of reviews. The method 100 may begin atblock 102 and end at block 112. Consistent with various embodiments, themethod 100 can include an identifying block 104, a grouping block 106, adetermining block 108, and an establishing block 110.

Consistent with various embodiments, at block 101 the method 100 mayinclude identifying a variant feature of an item having a first set ofperspective data. The item may include a product (hardware or software),component, service, commodity, material, article, software, technology,technical data, process, material, establishment, organization,building, location, or the like. As an example, in certain embodiments,the item may be a consumer product such as a smart phone, a television,a car, a bicycle, or the like. As an additional example, the item may bea restaurant, hotel, college, retail store, or internet shopping portal.As described herein, the item may have a first set of perspective data.In certain embodiments, the first set of perspective data may include afirst set of reviews. The first set of reviews may be evaluations,assessments, or commentaries, editorials, opinion pieces, social media,or other content for the item that assesses the relative quality ormerit of the item. The first set of reviews may include reviews of anumber of different media formats. For example, the first set of reviewsmay include reviews in the form of videos, audio files (e.g., podcasts),web site articles, magazine articles, television or radio broadcasts, orthe like. In certain embodiments, one or more reviews of the first setof reviews may include a quantitative rating such as a number or letterto indicate the overall merit of the item. In certain embodiments, thefirst set of reviews may include both user reviews (e.g., reviewswritten by consumers or customers based on experience with the item) andcritic reviews (e.g., reviews written by professional journalists orcritics.)

In certain embodiments, the variant feature may be a characteristic oraspect of an item having a first set of reviews. More specifically, thevariant feature may be a characteristic of the item that is discussed inone or more reviews of the first set of reviews. In certain embodiments,the variant feature may be configured to change (e.g., vary) withrespect to a predetermined, fixed criterion. For example, consider a setof reviews for a television. Many reviews written early after theconsumer release of the television may criticize the sound quality ofthe “Integrated Audio Basic” built-in speakers as low-volume and lackingbass. In response, the manufacturer of the television may revise latermodels of the same SKU (stock-keeping unit) to have the better quality“Super Audio Experience” built-in speakers. After this change, laterreviews may mention that the built-in speakers were excellent, having afull range of sound and volume options. Accordingly, “built-in speakers”could be identified as a variant feature.

Further, in certain embodiments, the variant feature may include two ormore variable elements dependent on the predetermined-fixed criterion.For instance, in the previous example, the “Super Audio Experience”built-in speakers may be identified as a first variable element, and the“Integrated Audio Basic” built-in speakers may be identified as a secondvariable element. As described above, the inclusion of the “IntegratedAudio Basic” speakers or the “Super Audio Experience” speakers in one ofthe televisions is dependent on time (e.g., when the television wasmanufactured). As an additional example, consider a restaurant with twolocations; one in the town of Hill Valley and another in the town ofSunnydale. Reviews for the restaurant may praise the prompt service andexcellent staff of the Hill Valley restaurant, while criticizing theslow service and unfriendly staff of the Sunnydale location.Accordingly, the variant feature may be identified as “restaurantservice” configured to vary with respect to location. Further, the firstvariable element may be identified as the Hill Valley location and thesecond variable element may be identified as the Sunnydale location.

Consistent with various embodiments, the method may include identifyingthe variant feature of an item having a first set of reviews. In certainembodiments, identifying the variant feature of the item may includeusing a natural language processing technique to parse the content ofthe first set of reviews. Parsing the content of the first set ofreviews may include analyzing the linguistic content of an article,audio file, or video. In certain embodiments, the natural languageprocessing technique may be a software tool, widget, or other programconfigured to parse the first set of reviews. In certain embodiments,the natural language processing technique can be configured to analyze asemantic feature and a syntactic feature of the portion of the set ofsearch results and the portion of the search query. The natural languageprocessing technique can be configured to recognize keywords, contextualinformation, and metadata tags associated with the first set of reviews.In certain embodiments, the natural language processing technique can beconfigured to analyze summary information, keywords, figure captions,and text descriptions included in first set of reviews, and usesyntactic and semantic elements present in this information to determinethe variant feature. The syntactic and semantic elements can includeinformation such as word frequency, word meanings, text font, italics,hyperlinks, proper names, noun phrases, parts-of-speech, and the contextof surrounding words. Other syntactic and semantic elements are alsopossible. Additionally, the natural language processing technique may beconfigured to collect and parse information other than the first set ofreviews. For instance, the natural language processing technique maygather data from websites, product specifications, user profiles, socialmedia, and the like.

Based on the analyzed metadata, contextual information, syntactic andsemantic elements, and other data, the natural language processingtechnique can be configured to determine a set of shared characteristicsof the first set of reviews. The set of shared characteristics mayinclude keywords or concepts that are mentioned multiple times in thefirst set of reviews. For instance, the natural language processingtechnique may determine that the set of shared characteristics arementioned a number of times greater than a frequency threshold. Incertain embodiments, the frequency threshold may be predetermined. Incertain embodiments, the frequency threshold may be adjustable based onthe subject matter of the first set of reviews, media format of thereviews, or other factor. In certain embodiments, keywords and conceptsmay not be explicitly mentioned in the set of reviews, but may beinferred based on contextual and semantic information of the first setof reviews. Other methods of determining the set of sharedcharacteristics are also possible.

In response to determining the set of shared characteristics of the setof reviews, the method 100 may include selecting a first sharedcharacteristic from the set of shared characteristics as the variantfeature. In certain embodiments, selecting the first sharedcharacteristic from the set of shared characteristics can includedetermining, based on semantic information analyzed by the naturallanguage processing technique, that the first shared characteristic hasa pertinence value greater than a pertinence threshold. Put differently,the method 100 can analyze the contextual meaning (e.g., semanticinformation) of each shared characteristic of the set of sharedcharacteristics, and choose the shared characteristic that most greatlyinfluenced the sentiment of the review. The pertinence value may, incertain embodiments, be an integer value between 1 and 100. Similarly,the pertinence threshold may be a predetermined pertinence value. Ashared characteristic having a pertinence value greater than thepertinence threshold may be selected as the variant feature.

For instance, consider the following example. The set of sharedcharacteristics for a set of reviews for a cell phone may, in certainembodiments, include key words and phrases such as battery life, screenresolution, app availability, call quality, and messaging functions. Thenatural language processing technique may analyze semantic informationand contextual data for each of the key words and phrases of the set ofshared characteristics, and determine a pertinence value for each sharedcharacteristic. In certain embodiments, the natural language processingtechnique may determine that “battery life” is mentioned along with thephrases “notable flaw,” “disappointingly short” and “key weakness.”Accordingly, in certain embodiments, the method 100 may includeassigning a pertinence value of 84 to the shared characteristic of“battery life.” Further, in certain embodiments, the method 100 mayinclude determining that the pertinence value of 84 is above thepredetermined pertinence threshold of 75. Accordingly, “battery life”may be selected as the variant feature.

Consistent with various embodiments, at block 106 the method 100 mayinclude grouping, based on the variant feature, the first set of reviewsinto a first group and a second group. Grouping the first set of reviewsinto the first group and the second group may include organizing thereviews of the first set of reviews based on the variant feature. Morespecifically, in certain embodiments, grouping the first set of reviewscan include sorting reviews that are associated with a first variableelement of the variant feature into the first group, and sorting reviewsthat are associated with a second variable element of the variantfeature into the second group. In certain embodiments, the naturallanguage processing technique may be configured to analyze the first setof reviews, and determine whether reviews included in the first set ofreviews are more closely related to the first variable element or thesecond variable element. More specifically, in certain embodiments, thenatural language processing technique may perform a frequency analysisfor a particular review of the first set of reviews, and determinewhether terms or phrases semantically or syntactically related to thefirst variable element or the second variable element are mentioned withgreater frequency. Consider once again the example discussed above inwhich the variant feature is determined to be “restaurant service,” thefirst variable element is determined to be “Hill Valley” and the secondvariable element is determined to be “Sunnydale.” In certainembodiments, the natural language processing technique may analyze thefirst set of reviews, and sort reviews that discuss the “Hill Valley”restaurant location into the first group, and sort reviews that discussthe “Sunnydale” restaurant location into the second group. Other methodsof grouping the first set of reviews are also possible.

Consistent with various embodiments, at block 108 the method 100 mayinclude determining a set of relevancy scores for a group of the firstset of reviews. More specifically, the method 100 can includedetermining a first set of relevancy scores for the first group of thefirst set of reviews, and a second set of relevancy scores for thesecond group of the first set of reviews. In certain embodiments, themethod 100 may include determining a relevancy score for each review ofa particular group of reviews. In certain embodiments, the method 100may include determining an overall relevancy score for an entire groupof reviews. Generally, the first set of relevancy scores may be one ormore numerical values that indicate the relative significance,notability, or quality of a group of reviews. Put differently, therelevancy scores may represent the relevance of the group of reviewswith respect to a user. As an example, the first set of relevancy scoresmay be expressed as an integer between 1 and 100, wherein greaternumbers indicate greater relevance (e.g., with respect to a user), whilelesser numbers indicate lesser relevance.

In certain embodiments, determining the set of relevancy scores caninclude parsing a group (e.g., the first or second group) of the firstset of reviews. Parsing the group of the first set of reviews caninclude using a natural language processing technique, as describedherein, to analyze semantic content, syntactic content, and metadata ofthe group. As an example, parsing semantic content may includedetermining the relative sentiment (e.g., attitude, position, opinion,emotions) of a review based on an analysis of the contextualinformation, linguistic data, and semantic elements found in the review.As an example, a review that includes words and phrases such as“lacking,” “poor,” and “unimpressive” may be characterized as having asubstantially negative sentiment, while a review that includes words andphrases such as “phenomenal,” “above average,” and “impressive” may becharacterized as having a substantially positive sentiment. In certainembodiments, natural language processing technique may determine asentiment factor for the review. The sentiment factor may be an integervalue that characterizes the overall attitude of the review with respectto the item. For instance, in certain embodiments, the sentiment factormay be an integer value between 1 and 10, wherein lower integersindicate a generally lower (e.g., substantially negative, orunfavorable) sentiment, and higher integers indicate a generally higher(e.g., substantially positive, or favorable) sentiment. In certainembodiments, the variant feature may be determined based on thesentiment factor (e.g., a sentiment factor higher than a sentimentthreshold, or a substantial change in sentiment factor).

As an additional example, parsing metadata may include analyzingadditional information coupled with a particular review. For instance,in certain embodiments, a quantitative rating coupled with the review(e.g., 8/10, A+, etc.) may also be identified when parsing metadata ofthe group of reviews, and can be used in determining relevancy scoresfor the group of reviews. Similarly, a particular product may have adefect or other issue recognized by the manufacturer, and may be taggedto indicate the issue. It may also be determined that an associated dateor location of an item may affect the potential relevance of the item toa user (e.g., recent reviews for a product have identified a problem notpresent in older models).

Based on the parsed semantic content, syntactic content, and metadata ofthe group, the method 100 can include calculating the set of relevancyscores. Calculating the set of relevancy scores may include using analgorithm or other technique configured to weight the parsed semanticcontent, syntactic content, and metadata, and compute the set ofrelevancy scores using the calculated weights. As an example, for agroup including three reviews, the algorithm may compute a set ofrelevancy scores of 67, 49, 88, and 91 respectively. In response tocalculating the relevancy scores, the method 100 may include assigningthe set of relevancy scores to the group of reviews. Assigning the setof relevancy scores to the group of reviews may include designating aparticular relevancy score as corresponding to a corresponding review ofthe group of reviews. For example, the method 100 may assign therelevancy score 67 to a first review, the relevancy score 49 to a secondreview, the relevancy score 88 to a third review, and the relevancyscore 91 for a fourth review. Other methods of calculating the set ofrelevancy scores are also possible.

Consistent with various embodiments, at block 110 the method 100 mayinclude establishing, using at least one of the calculated relevancyscores, a second set of reviews configured to be a subset of the firstset of reviews. Establishing the second set of reviews may includeorganizing, creating, promoting, or indicating a portion of reviews(e.g., the second set) of reviews such that they are distinct from thefirst set of reviews. Generally, the second set of reviews may be aportion of the first set of reviews that are relevant, notable, orsignificant (e.g., with respect to a user). More specifically,establishing the second set of reviews may include determining that oneor more reviews of the first set of reviews have relevancy scoresgreater than or equal to a first relevancy threshold, and that one ormore reviews of the first set of reviews have relevancy scores less thanthe first relevancy threshold. Accordingly, the method 100 may includefiltering the first set of reviews to remove the reviews that haverelevancy scores below the relevancy threshold. As an example, incertain embodiments, the relevance threshold may be 75. Consider onceagain the example above, wherein a first review has a relevancy score of67, a second review has a relevancy score of 49, a third review has arelevancy score of 88, and a fourth review has a relevancy score of 91.In certain embodiments, the first and second reviews may be filteredfrom the set of reviews. In certain embodiments, in response todetermining that particular reviews of the first set of reviews haverelevancy values below the relevancy threshold, the method may includetagging those reviews with a marker, flag, or other indicator torepresent that those reviews are of lesser relevance. Other methods ofestablishing the second set of reviews are also possible.

In certain embodiments, in response to establishing the second set ofreviews, the method 100 may be configured to provide the second set ofreviews to a user. Providing the second set of reviews to a user may bedone in one of a number of ways. In certain embodiments, a review reportcontaining the second set of reviews may be provided to the user viaelectronic mail, smartphone alert, web page notification, or the like.In certain embodiments, a chart, graph, timeline or other visualrepresentation including the second set of reviews is also possible.

FIG. 2 is an illustration of an example implementation of a method formanaging reviews, consistent with various embodiments. Aspects of FIG. 2are directed toward a review timeline 200 for managing reviews for anitem. More specifically, FIG. 2 depicts a review timeline for organizingand displaying reviews for a hotel. As shown in FIG. 2, the reviewtimeline 200 can include a first review 202, a second review 204, athird review 206, a fourth review 208, a fifth review 210, a sixthreview 212, a seventh review 214, an eighth review 216, a pre-renovationperiod (second variable element) 240, a full interior renovation period(variant feature) 250, and a post-renovation period (first variableelement) 260.

Aspects of the present disclosure, in certain embodiments, are directedtoward generating a chronological representation for a set of reviewswith respect to a variant feature, a first variable element, and asecond variable element. Accordingly, in certain embodiments, thechronological representation may be a review timeline 200, as shown inFIG. 2. The review timeline may display a time interval annotated withone or more reviews. In certain embodiments, the reviews may be placedalong the timeline based on the date that they were written. In certainembodiments, the natural language processing technique described hereinmay be configured to select and provide reviews that have relevancyscores above a second relevancy threshold.

Consider the following example. A hotel may receive generally poorreviews over a six month period between January and June. The reviewsmay criticize the hotel's lack of cleanliness, antiquated interior, anddeteriorated condition. For example, a first review 202 may rate thehotel a 4/10, citing poor lighting and weak water pressure, and a secondreview 204 may rate the hotel a 2/10, discussing an unpleasant smell andthin walls. Similarly, a third review 206 may give the hotel a 1/10rating based on peeling wall paper and weak water pressure, and a fourthreview 208 may rate the hotel a 3/10 because of slow internet and poorlighting. Over a two month period between July and August, the hotel mayundergo a full interior renovation. After the renovation, the overallsentiment of the reviews may become more positive. For instance, a fifthreview may rate the hotel an 8/10, praising the fast internet and freshsmell, and a sixth review 212 may give the hotel a 9/10 rating based ona “clean feeling” and strong water pressure. Additionally, a seventhreview 214 may rate the hotel a 7/10 for reasons including a “beautifullobby,” and an eighth review 216 may give the hotel a 10/10 rating,speaking highly of the large room, windows, and lack of noise.

Consistent with various embodiments, aspects of the present disclosureare directed toward identifying a variant feature for the hotel usingthe set of reviews. As discussed herein, identifying the variant featuremay include using a natural language processing technique to parse theset of reviews and evaluate the sentiment of the set of reviews, as wellas identify shared characteristics. In certain embodiments, the set ofshared characteristics may include specific words or phrases that arementioned in multiple reviews of the set of reviews, such as “waterpressure” For example, the set of shared characteristics may include“water pressure” and “odor.” In certain embodiments, the set of sharedcharacteristics may be inferred by the natural language processingtechnique based on the semantic and syntactic content of the reviews.For example, the natural language processing technique may determinethat “interior condition” is a general theme discussed in the set ofreviews, and identify it as a shared characteristic.

Further, the natural language processing technique may analyze thesentiment of the set of reviews, and determine that the sentimentregarding the shared characteristic of “interior condition” has changedfrom substantially negative (e.g., sentiment factors below 5) tosubstantially positive (e.g., sentiment factors 5 or greater) based onearly reviews containing terms such as “poor” and “unpleasant,” whilelater reviews contain terms such as “fresh,” “clean,” and “beautiful.”Further, in certain embodiments, the natural language processingtechnique may be configured to collect data from sources other than theset of reviews, such as the hotel websites, travel guides, and the like.For instance, in the present example, the natural language processingtechnique may determine based on information on the website of thehotel, that an interior renovation was conducted between July andAugust. The natural language processing technique may correlate theinterior renovation with the determined change in sentiment regardingthe “interior condition,” and determine the full interior renovation asthe variant feature 250. Similarly, the natural language processingtechnique may determine the pre-renovation period as the second variableelement 240, and the post renovation period as the first variableelement 260.

As described herein, based on the variant feature 250, the firstvariable element 260, and the second variable element 240, aspects ofthe present disclosure are directed toward grouping the first set ofreviews into a first group and a second group. Grouping the first set ofreviews may include sorting reviews associated with the first variableelement into a first group, and reviews associated with the secondvariable element into a second group. Accordingly, in the presentexample, the first, second, third, and fourth reviews that were writtenin the pre-renovation period (e.g., the second variable element) may besorted into the second group, while the fifth, sixth, seventh, andeighth reviews written in the post-renovation period (e.g., the firstvariable element) may be sorted into the first group.

Aspects of the present disclosure are directed toward determining afirst set of relevancy scores for the first group, and a second set ofrelevancy scores for the second group. As described herein, therelevancy scores may be calculated by the natural language processingtechnique using semantic content, syntactic content, and metadata forboth groups of reviews. For example, the relevancy scores may becomputed based on individual ratings or scores (e.g., metadata) of oneor more reviews in a particular group. For instance, in the presentexample, the first group may be assigned a relevancy score of 8.5 (e.g.,the mean of the reviews included in the first group) and the secondgroup may be assigned a relevancy score of 2.5 (e.g., the mean of thereviews included in the second group). In certain embodiments, eachreview of a particular group may be assigned a relevancy score. Othermethods of calculating the relevancy scores are also possible.

Aspects of the present disclosure are directed toward establishing asecond set of reviews using at least one of the first and second sets ofrelevancy scores. Establishing the second set of reviews may be done inone of a number of ways. For example, in the present example, reviewsincluded in a group with a relevancy score greater than a relevancythreshold of 7.5 (e.g., the fifth, sixth, seventh, and eighth reviews)may be provided in a “Suggested Reviews” category (e.g., on a travelwebsite or the like.) Similarly, in certain embodiments, a reviewtimeline 200 may be displayed, and reviews in the second set of reviewsmay be highlighted or starred to notify users. Other methods ofestablishing the second set of reviews are also possible.

FIG. 3 is a diagram illustrating an example system architecture 300 formanaging reviews, consistent with embodiments of the present disclosure.Aspects of FIG. 3 are directed toward grouping and scoring a first setof reviews for an item using an identified variant feature, andestablishing a second set of reviews. As shown in FIG. 3, in certainembodiments, the example system architecture 300 can include a variantfeature identification system 310, a review content parsing module 312,a shared characteristic determination module 314, a review database 316,a variant feature selection module 318, a grouping system 320, a firstand second group sorting module 322, a relevancy score determinationsystem 330, a group content parsing module 322, a relevancy scorecalculation module 324, a relevancy score assignment module 326, areview establishing system 340, a relevancy score/thresholddetermination module 342, a filtering module 344, and feedback data 355.

Consistent with various embodiments, the variant feature identificationsystem 310 of FIG. 3 may substantially correspond with identifying block104 of FIG. 1. The review content parsing module 312 may be configuredto use a natural language processing technique to analyze semantic andsyntactic content of a first set of reviews. The first set of reviewsmay, in certain embodiments, be stored on a review database 316accessible to the variant feature identification system 310. In responseto parsing the first set of reviews, the shared characteristicdetermination module 314 may be configured to determine a set of sharedcharacteristics of the first set of reviews. The variant featureselection module 318 may be configured to select a first sharedcharacteristic as the variant feature. In certain embodiments, the firstshared characteristic may be selected as the variant feature based on apertinence value greater than a pertinence threshold.

As described herein, in certain embodiments, the natural languageprocessing technique may be configured to evaluate the sentiment of thefirst set of reviews, and determine a sentiment factor for one or morereviews of the first set of reviews. Accordingly, in certainembodiments, the variant feature may be determined based on thesentiment of the first set of reviews. For example, in certainembodiments, the natural language processing technique may determinethat there is a change in the sentiment factor of the first set ofreviews in response to a triggering event. The change in the sentimentfactor may, for instance, be an increase or decrease in the sentimentfactor greater than a sentiment change threshold. Accordingly, thenatural language processing technique may, in certain embodiments,select the triggering event as the variant feature.

Consistent with various embodiments, the grouping system 320 maysubstantially correspond with grouping block 106 of FIG. 1. The groupingsystem 320 may be configured to group the first set of reviews into afirst group and a second group based on the variant feature. Morespecifically, the first and second group sorting module 322 may beconfigured to sort reviews associated with a first variable element ofthe variable feature into the first group, and sort reviews associatedwith a second variable element of the variable feature into the secondgroup. In certain embodiments, the first variable element of the variantfeature may be associated with a sentiment factor greater than (or equalto) a sentiment threshold, and the second variable element may beassociated with a sentiment factor less than a sentiment threshold. Thesentiment threshold may be a predetermined sentiment factor. Forexample, in certain embodiments, the sentiment threshold may be 5.Accordingly, the first variable element may be associated with asentiment factor greater than or equal to 5 (e.g., generally positivereviews), while the second variable element is associated with asentiment factor less than 5 (e.g., generally negative reviews).

Consistent with various embodiments, the relevancy score determinationsystem 330 may substantially correspond with the determining block 108of FIG. 1. The group content parsing module 322 may be configured toparse, using the natural language processing technique, semantic andsyntactic content of a group (e.g., the first and second group). Basedon syntactic content, syntactic content, and metadata for the group, therelevancy score calculation module 324 may be configured to calculate aset of relevancy scores for the group. In response to calculating theset of relevancy scores, the relevancy score assignment module 326 mayassign the relevancy scores to the group.

Consistent with various embodiments, the review establishing system 340may substantially correspond to the establishing block 110 of FIG. 1.The relevancy score/threshold determination module 342 may be configuredto determine that the first set of relevancy scores of the first groupis greater than a first relevancy threshold, and that the second set ofrelevancy scores of the second group is lower than the first relevancythreshold. Accordingly, the filtering module 344 can be configured tofilter the first set of reviews to remove the second group. In certainembodiments, the review establishing system may be configured totransmit feedback data 355 to the variant feature identification system310. The feedback data 355 may contain information regarding therelevancy scores, second set of reviews, first and second groups, andfirst and second variable elements that may allow the variant featureidentification system 310 to refine the process of variant featureidentification.

FIG. 4 is a flowchart illustrating a method 400 for analyzing andmanaging a set of item reviews, consistent with embodiments of thepresent disclosure. Aspects of FIG. 4 are directed toward evaluating afirst and a second lot of perspective data to ascertain a sentimentfluctuation. The method 400 may begin at block 402 and end at block 499.Consistent with various embodiments, the method 400 can include a firstcollecting block 410, an introducing block 420, a second collectingblock 430, and an evaluating block 440.

Aspects of FIG. 4, in certain embodiments, are directed towardintroducing a variant feature to an item, collecting perspective datafrom before and after the introduction of the variant feature, andevaluating the perspective data to ascertain a sentiment fluctuationbased on information relevant to the variant feature. In certainembodiments, at block 410 the method 400 may include collecting a firstlot of perspective data for an item. The first lot of perspective datamay include reviews, commentary, editorials, social media data, opinionpieces, and other content that assesses the relative quality or merit ofthe item.

At block 420, the method 400 may include introducing a variant featureto the item to constitute a modified item. In certain embodiments,introducing the variant feature to the item may be based on the firstlot of perspective data. For example, in certain embodiments, at block421 the method 400 may include analyzing the first lot of perspectivedata using a natural language processing technique configured to parsesemantic and syntactic content. In response to analyzing the first lotof perspective data, the method 400 may include extracting, at block422, revision candidate data for the item. The revision candidate datafor the item may be information including a list of potential aspects orcharacteristics of the item that may be revised, improved, or updated.Based on the revision candidate data for the item, at block 423 themethod 400 can include determining a variant feature for the item. Thevariant feature may, in certain embodiments, be introduced to revise,improve, or update the item.

In certain embodiments, the variant feature may be introduced to theitem in response to a triggering event. For example, in certainembodiments, in response to analyzing the first lot of perspective datafor the item at block 421, at block 424 the method 400 can includedetermining that a first element of the first lot of perspective datafor the item is characterized by a sentiment score above a sentimentthreshold. Similarly, at block 425, the method 400 can includedetermining, in response to analyzing the first lot of perspective dataat block 421, that a second element of the first lot of perspective datafor the item is characterized by a sentiment score below a sentimentthreshold. As described herein, aspects of the present disclosure aredirected toward analyzing the semantic and syntactic content ofperspective data to determine a relative sentiment (e.g., based on thetone, diction, quantitative rating, and other characteristics).Accordingly, the sentiment score may be an integer value between 0 and100, wherein lower integers indicate a relatively unfavorable (e.g.,negative) sentiment, and higher integers indicate a relatively favorable(e.g., positive sentiment). The first element may, for instance, be acomponent of the item that is associated with a substantially highsentiment score, but it may be desirable to revise the first element forcost/benefit reasons. Similarly, the second element may be a componentof the item that is associated with a substantially low sentiment score,and be desirable to revise in order to improve the quality of the item.

As described herein, introducing the variant feature may be performed inone of a number of ways. For example, at block 426, the method 400 mayinclude identifying, based on the collected revision candidate data forthe item, a first component absent from the item. Accordingly,introducing the variant feature may, at block 427, include adding thefirst component to the item. For instance, in certain embodiments, theitem may be a tablet computer. Initially, the tablet computer may not beequipped with a physical keyboard. Adding the first component mayinclude supplementing future models of the tablet computer with aphysical keyboard. In certain embodiments, at block 426, the method 400may include identifying, based on the collected revision candidate datafor the item, a second component included in the item. Introducing thevariant feature may, at block 428, include removing the second componentfrom the item. For instance, the item may be a restaurant, and thesecond component may be a lutefisk dish available on the menu.Accordingly, removing the second component may include removing thelutefisk dish from the menu.

At block 430, in certain embodiments, the method 400 may includecollecting a second lot of perspective data for the modified item. Asdescribed herein, the second lot of perspective data may includereviews, commentary, editorials, social media data, opinion pieces, andother content that assesses the relative quality or merit of themodified item.

At block 440, in certain embodiments, the method 400 may includeevaluating the first and second lots of perspective data to ascertain asentiment fluctuation based on information relevant to the variantfeature. The sentiment fluctuation may be a relative change in theopinion, tone, attitude, or feeling expressed by the perspective datawith respect to the item. In certain embodiments, the sentimentfluctuation may be brought on by the introduction of the variantfeature. For example, as described herein, the rating of a review for ahotel may increase from 3 stars to 4 stars in response to a change inthe management at the hotel.

In certain embodiments, at block 441, evaluating the first and secondlots of perspective data may include analyzing the first and second lotsof perspective data using a natural language processing technique. Basedon semantic and syntactic content of the first and second lots ofperspective data, at block 422 the method 400 may include computing afirst sentiment score for the first lot of perspective data and a secondsentiment score for the second lot of perspective data. As describedherein, the first and second sentiment scores may be computed based ondiction, tone, metadata, and other content associated with the first andsecond lot of perspective data. At block 443, the method 400 may includecomparing the first sentiment score with the second sentiment score, andgenerating a first evaluation output. The first evaluation output mayinclude an assessment of the relative effect of the variant feature. Forinstance, the evaluation output may rate the effectiveness of thevariant feature based on the first and second sentiment score. Forexample, in a situation where the second sentiment score issignificantly greater (e.g., beyond a threshold value) than the firstsentiment score, the variant feature may be evaluated to besubstantially effective. In a situation where the second sentiment scoreis not significantly greater (e.g., not beyond a threshold value) orbelow the first sentiment score, the variant feature may not beevaluated as being substantially effective.

In certain embodiments, as described herein the variant feature mayinclude a first variable element and a second variable element. Thefirst and second variable elements may correspond to a predetermined,fixed criterion (e.g., time, location). At block 444, the method 400 mayinclude instantiating the first variable element and the second variableelement. More specifically, the method 400 can include instantiating thefirst variable element for a first item and a second variable elementfor a second item. At block 445, the method 400 can include collecting athird lot of perspective data for the first item and a fourth lot ofperspective data for the second item. At block 446, the method caninclude using a natural language processing technique to analyze thethird and fourth lots of perspective data. Based on semantic andsyntactic content of the third and fourth lots of perspective data, atblock 447 the method 400 may include computing a third sentiment scorefor the third lot of perspective data and a fourth sentiment score forthe fourth lot of perspective data. At block 448, the method 400 caninclude generating a second evaluation output including an assessment ofthe relative effect of the first variable element and the secondvariable element.

Consistent with various embodiments, as described herein, the method 400can include collecting the first lot of perspective data for the itemfrom a first community of users, and collecting the second lot ofperspective data for the modified item from a second community of users.At block 449, the method 400 can include determining, by comparing thefirst community of users with the second community of users, asub-community of users. In certain embodiments, the sub-community ofusers may be included in both the first community of users and thesecond community of users. At block 450, the method 400 can includecollecting a subset of perspective data from the sub-community of users.At block 451, the method 400 can include assigning a weighting value tothe subset of perspective data. In certain embodiments, the weightingvalue may be an integer indicating a level of relevance or significanceof the subset of perspective data.

FIG. 5 depicts a high-level block diagram of a computer system 500 forimplementing various embodiments. The mechanisms and apparatus of thevarious embodiments disclosed herein apply equally to any appropriatecomputing system. The major components of the computer system 500include one or more processors 502, a memory 504, a terminal interface512, a storage interface 514, an I/O (Input/Output) device interface516, and a network interface 518, all of which are communicativelycoupled, directly or indirectly, for inter-component communication via amemory bus 506, an I/O bus 508, bus interface unit 509, and an I/O businterface unit 510.

The computer system 500 may contain one or more general-purposeprogrammable central processing units (CPUs) 502A and 502B, hereingenerically referred to as the processor 502. In embodiments, thecomputer system 500 may contain multiple processors; however, in certainembodiments, the computer system 500 may alternatively be a single CPUsystem. Each processor 502 executes instructions stored in the memory504 and may include one or more levels of on-board cache.

In embodiments, the memory 504 may include a random-access semiconductormemory, storage device, or storage medium (either volatile ornon-volatile) for storing or encoding data and programs. In certainembodiments, the memory 504 represents the entire virtual memory of thecomputer system 500, and may also include the virtual memory of othercomputer systems coupled to the computer system 500 or connected via anetwork. The memory 504 can be conceptually viewed as a singlemonolithic entity, but in other embodiments the memory 504 is a morecomplex arrangement, such as a hierarchy of caches and other memorydevices. For example, memory may exist in multiple levels of caches, andthese caches may be further divided by function, so that one cache holdsinstructions while another holds non-instruction data, which is used bythe processor or processors. Memory may be further distributed andassociated with different CPUs or sets of CPUs, as is known in any ofvarious so-called non-uniform memory access (NUMA) computerarchitectures.

The memory 504 may store all or a portion of the various programs,modules and data structures for processing data transfers as discussedherein. For instance, the memory 504 can store a perspective datamanagement application 550. In embodiments, the perspective datamanagement application 550 may include instructions or statements thatexecute on the processor 502 or instructions or statements that areinterpreted by instructions or statements that execute on the processor502 to carry out the functions as further described below. In certainembodiments, the perspective data management application 550 isimplemented in hardware via semiconductor devices, chips, logical gates,circuits, circuit cards, and/or other physical hardware devices in lieuof, or in addition to, a processor-based system. In embodiments, theperspective data management application 550 may include data in additionto instructions or statements.

The computer system 500 may include a bus interface unit 509 to handlecommunications among the processor 502, the memory 504, a display system524, and the I/O bus interface unit 510. The I/O bus interface unit 510may be coupled with the I/O bus 508 for transferring data to and fromthe various I/O units. The I/O bus interface unit 510 communicates withmultiple I/O interface units 512, 514, 516, and 518, which are alsoknown as I/O processors (IOPs) or I/O adapters (IOAs), through the I/Obus 508. The display system 524 may include a display controller, adisplay memory, or both. The display controller may provide video,audio, or both types of data to a display device 526. The display memorymay be a dedicated memory for buffering video data. The display system524 may be coupled with a display device 526, such as a standalonedisplay screen, computer monitor, television, or a tablet or handhelddevice display. In one embodiment, the display device 526 may includeone or more speakers for rendering audio. Alternatively, one or morespeakers for rendering audio may be coupled with an I/O interface unit.In alternate embodiments, one or more of the functions provided by thedisplay system 524 may be on board an integrated circuit that alsoincludes the processor 502. In addition, one or more of the functionsprovided by the bus interface unit 509 may be on board an integratedcircuit that also includes the processor 502.

The I/O interface units support communication with a variety of storageand I/O devices. For example, the terminal interface unit 512 supportsthe attachment of one or more user I/O devices 520, which may includeuser output devices (such as a video display device, speaker, and/ortelevision set) and user input devices (such as a keyboard, mouse,keypad, touchpad, trackball, buttons, light pen, or other pointingdevice). A user may manipulate the user input devices using a userinterface, in order to provide input data and commands to the user I/Odevice 520 and the computer system 500, and may receive output data viathe user output devices. For example, a user interface may be presentedvia the user I/O device 520, such as displayed on a display device,played via a speaker, or printed via a printer.

The storage interface 514 supports the attachment of one or more diskdrives or direct access storage devices 522 (which are typicallyrotating magnetic disk drive storage devices, although they couldalternatively be other storage devices, including arrays of disk drivesconfigured to appear as a single large storage device to a hostcomputer, or solid-state drives, such as flash memory). In someembodiments, the storage device 522 may be implemented via any type ofsecondary storage device. The contents of the memory 504, or any portionthereof, may be stored to and retrieved from the storage device 522 asneeded. The I/O device interface 516 provides an interface to any ofvarious other I/O devices or devices of other types, such as printers orfax machines. The network interface 518 provides one or morecommunication paths from the computer system 500 to other digitaldevices and computer systems; these communication paths may include,e.g., one or more networks 530.

Although the computer system 500 shown in FIG. 5 illustrates aparticular bus structure providing a direct communication path among theprocessors 502, the memory 504, the bus interface 509, the displaysystem 524, and the I/O bus interface unit 510, in alternativeembodiments the computer system 500 may include different buses orcommunication paths, which may be arranged in any of various forms, suchas point-to-point links in hierarchical, star or web configurations,multiple hierarchical buses, parallel and redundant paths, or any otherappropriate type of configuration. Furthermore, while the I/O businterface unit 510 and the I/O bus 508 are shown as single respectiveunits, the computer system 500 may, in fact, contain multiple I/O businterface units 510 and/or multiple I/O buses 508. While multiple I/Ointerface units are shown, which separate the I/O bus 508 from variouscommunications paths running to the various I/O devices, in otherembodiments, some or all of the I/O devices are connected directly toone or more system I/O buses.

In various embodiments, the computer system 500 is a multi-usermainframe computer system, a single-user system, or a server computer orsimilar device that has little or no direct user interface, but receivesrequests from other computer systems (clients). In other embodiments,the computer system 500 may be implemented as a desktop computer,portable computer, laptop or notebook computer, tablet computer, pocketcomputer, telephone, smart phone, or any other suitable type ofelectronic device.

FIG. 5 depicts several major components of the computer system 500.Individual components, however, may have greater complexity thanrepresented in FIG. 5, components other than or in addition to thoseshown in FIG. 5 may be present, and the number, type, and configurationof such components may vary. Several particular examples of additionalcomplexity or additional variations are disclosed herein; these are byway of example only and are not necessarily the only such variations.The various program components illustrated in FIG. 5 may be implemented,in various embodiments, in a number of different manners, includingusing various computer applications, routines, components, programs,objects, modules, data structures, etc., which may be referred to hereinas “software,” “computer programs,” or simply “programs.”

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present disclosurehave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

1. A computer implemented method for managing perspective data, themethod comprising: collecting a first lot of perspective data for anitem; introducing a variant feature to the item to constitute a modifieditem; collecting a second lot of perspective data for the modified item;and evaluating the first and second lots of perspective data toascertain a sentiment fluctuation based on information relevant to thevariant feature.
 2. The method of claim 1, wherein evaluating the firstand second lots of perspective data to ascertain a sentiment fluctuationincludes: analyzing, using a natural language processing techniqueconfigured to parse semantic and syntactic content, the first and secondlots of perspective data; computing, based on semantic and syntacticcontent of the first and second lots of perspective data, a firstsentiment score for the first lot of perspective data and a secondsentiment score for the second lot of perspective data; and generating,based on comparing the first sentiment score with the second sentimentscore, a first evaluation output including an assessment of the relativeeffect of the variant feature.
 3. The method of claim 2, whereinintroducing the variant feature is performed in response to: analyzing,using a natural language processing technique configured to parsesemantic and syntactic content, the first lot of perspective data for anitem; and determining that a first element of the first lot ofperspective data for the item is characterized by a sentiment scoreabove a sentiment threshold.
 4. The method of claim 2, whereinintroducing the variant feature is performed in response to: analyzing,using a natural language processing technique configured to parsesemantic and syntactic content, the first lot of perspective data for anitem; and determining that a second element of the first lot ofperspective data for the item is characterized by a sentiment scorebelow a sentiment threshold.
 5. The method of claim 2, wherein thevariant feature includes a first variable element and a second variableelement, the first and second variable elements corresponding to apredetermined fixed criterion.
 6. The method of claim 5, furthercomprising: instantiating the first variable element for a first itemand the second variable element for a second item; collecting a thirdlot of perspective data for the first item and a fourth lot ofperspective data for the second item; analyzing, using a naturallanguage processing technique configured to parse semantic and syntacticcontent, the third and fourth lots of perspective data; computing, basedon semantic and syntactic content of the third and fourth lots ofperspective data, a third sentiment score for the third lot ofperspective data and a fourth sentiment score for the fourth lot ofperspective data; and generating, based on comparing the third sentimentscore with the fourth sentiment score, a second evaluation outputincluding an assessment of the relative effect of the first variableelement and the second variable element.
 7. The method of claim 1,further comprising: analyzing, using a natural language processingtechnique configured to parse semantic and syntactic content, the firstlot of perspective data; extracting, in response to analyzing the firstlot of perspective data, revision candidate data for the item; anddetermining, based on the revision candidate data for the item, avariant feature for the item.
 8. The method of claim 7, whereinintroducing the variant feature to the item to constitute a modifieditem includes: identifying, based on the revision candidate data for theitem, a first component absent from the item; and adding, to the item,the first component.
 9. The method of claim 7, wherein introducing thevariant feature to the item to constitute a modified item includes:identifying, based on the revision candidate data for the item, a secondcomponent included in the item; and removing, from the item, the firstcomponent.
 10. The method of claim 1, further comprising: collecting thefirst lot of perspective data for the item from a first community ofusers; collecting, the second lot of perspective data for the modifieditem from a second community of users; determining, by comparing thefirst community of users with the second community of users, asub-community of users included in both the first community of users andthe second community of users; collecting a subset of perspective datafrom the sub-community of users; and assigning, to the subset ofperspective data collected from the sub-community of users, a weightingvalue. 11-20. (canceled)